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Classe « DataFrame »

Méthode pandas.DataFrame.to_dict

Signature de la méthode to_dict

def to_dict(self, orient: "Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index']" = 'dict', *, into: 'type[MutableMappingT] | MutableMappingT' = <class 'dict'>, index: 'bool' = True) -> 'MutableMappingT | list[MutableMappingT]' 

Description

help(DataFrame.to_dict)

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters
(see below).

Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
    Determines the type of the values of the dictionary.

    - 'dict' (default) : dict like {column -> {index -> value}}
    - 'list' : dict like {column -> [values]}
    - 'series' : dict like {column -> Series(values)}
    - 'split' : dict like
      {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
    - 'tight' : dict like
      {'index' -> [index], 'columns' -> [columns], 'data' -> [values],
      'index_names' -> [index.names], 'column_names' -> [column.names]}
    - 'records' : list like
      [{column -> value}, ... , {column -> value}]
    - 'index' : dict like {index -> {column -> value}}

    .. versionadded:: 1.4.0
        'tight' as an allowed value for the ``orient`` argument

into : class, default dict
    The collections.abc.MutableMapping subclass used for all Mappings
    in the return value.  Can be the actual class or an empty
    instance of the mapping type you want.  If you want a
    collections.defaultdict, you must pass it initialized.

index : bool, default True
    Whether to include the index item (and index_names item if `orient`
    is 'tight') in the returned dictionary. Can only be ``False``
    when `orient` is 'split' or 'tight'.

    .. versionadded:: 2.0.0

Returns
-------
dict, list or collections.abc.MutableMapping
    Return a collections.abc.MutableMapping object representing the
    DataFrame. The resulting transformation depends on the `orient`
    parameter.

See Also
--------
DataFrame.from_dict: Create a DataFrame from a dictionary.
DataFrame.to_json: Convert a DataFrame to JSON format.

Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2],
...                    'col2': [0.5, 0.75]},
...                   index=['row1', 'row2'])
>>> df
      col1  col2
row1     1  0.50
row2     2  0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': row1    1
         row2    2
Name: col1, dtype: int64,
'col2': row1    0.50
        row2    0.75
Name: col2, dtype: float64}

>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]]}

>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]

>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}

>>> df.to_dict('tight')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
 'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
             ('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])

If you want a `defaultdict`, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
 defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]

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